Devices that collect and process data “at the edge” of the network are quickly
becoming a requirement for the next era of the IoT.
Consumers and organizations are already benefitting from a growing number of
smart, connected internet of things (IoT) devices to improve lives and drive
greater efficiencies, safety and convenience. The near-zero latencies demanded
by real-time applications such as industrial robotics and connected vehicles
cannot be achieved across wide area networks. At the same time, the enormous
volumes of data generated by IoT applications consume significant amounts of
costly network bandwidth and data center storage. And as increasing volumes of
sensitive data are transported to and from the cloud, security becomes a
growing concern. All of this is putting the traditional cloud computing model
under pressure. It’s becoming obvious that moving all of the increasing
amount of data generated by sensors from the end-user location to the cloud
and back is no longer viable.
A better approach is to complement the cloud by performing more of this
processing at the user location—at the edge. The edge operates on “instant
data” generated in real-time and processed by the IoT devices themselves. The
smallest microcontrollers and microprocessors can now run machine learning
(ML) in the device, thereby reducing latencies associated with cloud
computing. Even with no connection, your smart door lock, for example, unlocks
when it recognizes your face because the machine learning runs on an edge
device where your personal images are stored. And your private data, including
your comings and goings, can stay at the edge without leaving your home.
Continue reading this free eBook (209 pages).
copy now, and discover edge computing applications and use cases.
The Road to the Edge
The edge has great potential to change how we interact with our world in a
more productive, safe and efficient way. From a technology perspective, what
do you need to have? What are the components of a well-architected edge
computing system? How do you achieve optimal security, energy efficiency,
connectivity and machine learning intelligence?
I worked with more than 30 experts at NXP to answer these questions and we
shared our insights in
Essentials of Edge Computing. Wide-ranging topics are covered in this 200+ page ebook, including hardware
and software architectures, security, machine learning, connectivity, device
life-cycle management, energy efficiency and optimization, HMI and development
tools. Use cases are detailed for local voice, 5G for Industry 4.0, wearables,
time-sensitive networking and intelligent connected vehicles. You can download
it at no cost today.